{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T02:58:19Z","timestamp":1774493899524,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,12]],"date-time":"2023-11-12T00:00:00Z","timestamp":1699747200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["U1304402"],"award-info":[{"award-number":["U1304402"]}]},{"name":"National Natural Science Foundation of China","award":["41977284"],"award-info":[{"award-number":["41977284"]}]},{"name":"National Natural Science Foundation of China","award":["2019-378-16"],"award-info":[{"award-number":["2019-378-16"]}]},{"name":"Natural Science and Technology Project of Natural Resources Department of Henan Province","award":["U1304402"],"award-info":[{"award-number":["U1304402"]}]},{"name":"Natural Science and Technology Project of Natural Resources Department of Henan Province","award":["41977284"],"award-info":[{"award-number":["41977284"]}]},{"name":"Natural Science and Technology Project of Natural Resources Department of Henan Province","award":["2019-378-16"],"award-info":[{"award-number":["2019-378-16"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral images\u2019 (HSIs) classification research has seen significant progress with the use of convolutional neural networks (CNNs) and Transformer blocks. However, these studies primarily incorporated Transformer blocks at the end of their network architectures. Due to significant differences between the spectral and spatial features in HSIs, the extraction of both global and local spectral\u2013spatial features remains incomplete. To address this challenge, this paper introduces a novel method called TransHSI. This method incorporates a new spectral\u2013spatial feature extraction module that leverages 3D CNNs to fuse Transformer to extract the local and global spectral features of HSIs, then combining 2D CNNs and Transformer to capture the local and global spatial features of HSIs comprehensively. Furthermore, a fusion module is proposed, which not only integrates the learned shallow and deep features of HSIs but also applies a semantic tokenizer to transform the fused features, enhancing the discriminative power of the features. This paper conducts experiments on three public datasets: Indian Pines, Pavia University, and Data Fusion Contest 2018. The training and test sets are selected based on a disjoint sampling strategy. We perform a comparative analysis with 11 traditional and advanced HSI classification algorithms. The experimental results demonstrate that the proposed method, TransHSI algorithm, achieves the highest overall accuracies and kappa coefficients, indicating a competitive performance.<\/jats:p>","DOI":"10.3390\/rs15225331","type":"journal-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T02:46:47Z","timestamp":1699843607000},"page":"5331","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["TransHSI: A Hybrid CNN-Transformer Method for Disjoint Sample-Based Hyperspectral Image Classification"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6771-1665","authenticated-orcid":false,"given":"Ping","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}]},{"given":"Haiyang","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"},{"name":"Key Laboratory of Mine Spatio-Temporal Information and Ecological Restoration, Henan Polytechnic University Ministry of Natural Resources, Jiaozuo 454000, China"}]},{"given":"Pengao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}]},{"given":"Ruili","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/MGRS.2019.2912563","article-title":"Deep Learning for Classification of Hyperspectral Data: A Comparative Review","volume":"7","author":"Audebert","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1949","DOI":"10.1007\/s12524-019-01041-2","article-title":"Evaluation of Deep Learning CNN Model for Land Use Land Cover Classification and Crop Identification Using Hyperspectral Remote Sensing Images","volume":"47","author":"Bhosle","year":"2019","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Fong, A., Shu, G., and McDonogh, B. (2020, January 10\u201315). Farm to Table: Applications for New Hyperspectral Imaging Technologies in Precision Agriculture, Food Quality and Safety. Proceedings of the Conference on Lasers and Electro-Optics, Washington, DC, USA.","DOI":"10.1364\/CLEO_AT.2020.AW3K.2"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lu, B., Dao, P.D., Liu, J.G., He, Y.H., and Shang, J.L. (2020). Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture. Remote Sens., 12.","DOI":"10.3390\/rs12162659"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TGRS.2018.2849692","article-title":"GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection","volume":"57","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1109\/JSTARS.2020.3037070","article-title":"TDSSC: A Three-Directions Spectral\u2013Spatial Convolution Neural Network for Hyperspectral Image Change Detection","volume":"14","author":"Zhan","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"104514","DOI":"10.1109\/ACCESS.2019.2932117","article-title":"Multi-Scale CNN Based Garbage Detection of Airborne Hyperspectral Data","volume":"7","author":"Zeng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1186\/s13007-017-0233-z","article-title":"Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress","volume":"13","author":"Lowe","year":"2017","journal-title":"Plant Methods"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"031501","DOI":"10.1117\/1.JRS.15.031501","article-title":"Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: An updated review","volume":"15","author":"Peyghambari","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_10","first-page":"4099","article-title":"Local Manifold Learning-Based k-Nearest-Neighbor for Hyperspectral Image Classification","volume":"48","author":"Ma","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1109\/TGRS.2014.2319373","article-title":"Extended random walker-based classification of hyperspectral images","volume":"53","author":"Kang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1109\/LGRS.2005.846011","article-title":"On the impact of PCA dimension reduction for hyperspectral detection of difficult targets","volume":"2","author":"Farrell","year":"2005","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1109\/LGRS.2016.2581172","article-title":"Fast SVD With Random Hadamard Projection for Hyperspectral Dimensionality Reduction","volume":"13","author":"Menon","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"10394","DOI":"10.1109\/TGRS.2020.3048994","article-title":"Flexible Gabor-Based Superpixel-Level Unsupervised LDA for Hyperspectral Image Classification","volume":"59","author":"Jia","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2183","DOI":"10.1109\/JSTARS.2014.2329792","article-title":"A Study on the Effectiveness of Different Independent Component Analysis Algorithms for Hyperspectral Image Classification","volume":"7","author":"Falco","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yu, W., and Fang, Z. (2020). Multiple Kernel-Based SVM Classification of Hyperspectral Images by Combining Spectral, Spatial, and Semantic Information. Remote Sens., 12.","DOI":"10.3390\/rs12010120"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"258619","DOI":"10.1155\/2015\/258619","article-title":"Deep Convolutional Neural Networks for Hyperspectral Image Classification","volume":"2015","author":"Hu","year":"2015","journal-title":"J. Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4729","DOI":"10.1109\/TGRS.2017.2698503","article-title":"Learning and Transferring Deep Joint Spectral\u2013Spatial Features for Hyperspectral Classification","volume":"55","author":"Yang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","article-title":"Spectral\u2013Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework","volume":"56","author":"Zhong","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/LGRS.2019.2918719","article-title":"HybridSN: Exploring 3-D\u20132-D CNN Feature Hierarchy for Hyperspectral Image Classification","volume":"17","author":"Roy","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5384","DOI":"10.1109\/TGRS.2019.2899129","article-title":"Cascaded Recurrent Neural Networks for Hyperspectral Image Classification","volume":"57","author":"Hang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3639","DOI":"10.1109\/TGRS.2016.2636241","article-title":"Deep Recurrent Neural Networks for Hyperspectral Image Classification","volume":"55","author":"Mou","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2448","DOI":"10.1109\/TGRS.2020.3005623","article-title":"Geometry-Aware Deep Recurrent Neural Networks for Hyperspectral Image Classification","volume":"59","author":"Hao","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.neucom.2022.06.031","article-title":"Multi-feature fusion: Graph neural network and CNN combining for hyperspectral image classification","volume":"501","author":"Ding","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5966","DOI":"10.1109\/TGRS.2020.3015157","article-title":"Graph Convolutional Networks for Hyperspectral Image Classification","volume":"59","author":"Hong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8246","DOI":"10.1109\/TGRS.2020.2973363","article-title":"Nonlocal Graph Convolutional Networks for Hyperspectral Image Classification","volume":"58","author":"Mou","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3162","DOI":"10.1109\/TGRS.2019.2949180","article-title":"Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification","volume":"58","author":"Wan","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","first-page":"5501016","article-title":"Dual-Channel Capsule Generation Adversarial Network for Hyperspectral Image Classification","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1109\/LGRS.2017.2780890","article-title":"Semisupervised Hyperspectral Image Classification Based on Generative Adversarial Networks","volume":"15","author":"Zhan","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5046","DOI":"10.1109\/TGRS.2018.2805286","article-title":"Generative Adversarial Networks for Hyperspectral Image Classification","volume":"56","author":"Zhu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TGRS.2019.2934760","article-title":"HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation from Transformers","volume":"58","author":"He","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5518615","DOI":"10.1109\/TGRS.2021.3130716","article-title":"SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers","volume":"60","author":"Hong","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. arXiv."},{"key":"ref_36","first-page":"100694","article-title":"Classification of hyperspectral remote sensing images using different dimension reduction methods with 3D\/2D CNN","volume":"25","author":"Asker","year":"2022","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_37","first-page":"5512115","article-title":"Spectral-Spatial Self-Attention Networks for Hyperspectral Image Classification","volume":"60","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ge, H.M., Wang, L.G., Liu, M.Q., Zhu, Y.X., Zhao, X.Y., Pan, H.Z., and Liu, Y.Z. (2023). Two-Branch Convolutional Neural Network with Polarized Full Attention for Hyperspectral Image Classification. Remote Sens., 15.","DOI":"10.3390\/rs15030848"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3232","DOI":"10.1109\/TGRS.2019.2951160","article-title":"Spectral-Spatial Attention Network for Hyperspectral Image Classification","volume":"58","author":"Sun","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Roy, S.K., Deria, A., Hong, D., Rasti, B., Plaza, A., and Chanussot, J. (2022). Multimodal Fusion Transformer for Remote Sensing Image Classification. arXiv.","DOI":"10.1109\/TGRS.2023.3286826"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yang, L., Yang, Y., Yang, J., Zhao, N., Wu, L., Wang, L., and Wang, T. (2022). FusionNet: A Convolution-Transformer Fusion Network for Hyperspectral Image Classification. Remote Sens., 14.","DOI":"10.3390\/rs14164066"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"He, X., Chen, Y., and Lin, Z. (2021). Spatial-Spectral Transformer for Hyperspectral Image Classification. Remote Sens., 13.","DOI":"10.3390\/rs13030498"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5522214","DOI":"10.1109\/TGRS.2022.3221534","article-title":"Spectral\u2013Spatial Feature Tokenization Transformer for Hyperspectral Image Classification","volume":"60","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5514715","DOI":"10.1109\/TGRS.2021.3115699","article-title":"Spectral\u2013Spatial Transformer Network for Hyperspectral Image Classification: A Factorized Architecture Search Framework","volume":"60","author":"Zhong","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","unstructured":"Li, J., Xia, X., Li, W., Li, H., Wang, X., Xiao, X., Wang, R., Zheng, M., and Pan, X. (2022). Next-ViT: Next Generation Vision Transformer for Efficient Deployment in Realistic Industrial Scenarios. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4479","DOI":"10.1007\/s00521-022-07933-8","article-title":"3D residual spatial\u2013spectral convolution network for hyperspectral remote sensing image classification","volume":"35","author":"Firat","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"5539616","DOI":"10.1109\/TGRS.2022.3209182","article-title":"A Disjoint Samples-Based 3D-CNN With Active Transfer Learning for Hyperspectral Image Classification","volume":"60","author":"Ahmad","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","first-page":"5502505","article-title":"Integrating Coordinate Features in CNN-Based Remote Sensing Imagery Classification","volume":"19","author":"Zhang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"5506314","DOI":"10.1109\/TGRS.2021.3094867","article-title":"Nonoverlapped Sampling for Hyperspectral Imagery: Performance Evaluation and a Cotraining-Based Classification Strategy","volume":"60","author":"Cao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2008","DOI":"10.1109\/LGRS.2017.2747222","article-title":"On the Effect of Spatially Non-Disjoint Training and Test Samples on Estimated Model Generalization Capabilities in Supervised Classification with Spatial Features","volume":"14","author":"Geib","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1109\/TGRS.2016.2616489","article-title":"On the Sampling Strategy for Evaluation of Spectral-Spatial Methods in Hyperspectral Image Classification","volume":"55","author":"Liang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wang, W., Dai, J., Chen, Z., Huang, Z., Li, Z., Zhu, X., Hu, X.-h., Lu, T., Lu, L., and Li, H. (2022, January 19\u201324). InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52729.2023.01385"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1109\/JSTARS.2021.3133021","article-title":"Hyperspectral Image Classification-Traditional to Deep Models: A Survey for Future Prospects","volume":"15","author":"Ahmad","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_55","first-page":"2579","article-title":"Visualizing Data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"109153","DOI":"10.1016\/j.sigpro.2023.109153","article-title":"MS3Net: Multiscale stratified-split symmetric network with quadra-view attention for hyperspectral image classification","volume":"212","author":"Liu","year":"2023","journal-title":"Signal Process."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Mei, X., Pan, E., Ma, Y., Dai, X., Huang, J., Fan, F., Du, Q., Zheng, H., and Ma, J. (2019). Spectral-Spatial Attention Networks for Hyperspectral Image Classification. 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